Preferences For Product Features Research Articles

INVESTIGATION OF CUSTOMER PREFERENCE CHANGES FOLLOWING COVID-19 MARKET DISRUPTION USING ONLINE REVIEW ANALYSIS

AbstractCOVID-19 pandemic has continued to pose a challenge to the society for almost three years, adversely affecting all segments of population in a scale unseen in the recent decades. Over the course of COVID-19 pandemic, many people have lost their jobs and income. These social and economic impacts have disrupted the market, potentially altering people's attitudes towards different product features. Therefore, this paper investigates the changes in customer preferences on various features of different products, before and after COVID-19 pandemic, using online review analysis. The proposed framework consists of four stages. Firstly, product review data is collected and preprocessed. Secondly, customer interest in product features is explored using latent Dirichlet allocation. Thirdly, customer sentiment for these features is analyzed with Valence Aware Dictionary and sEntiment Reasoner. Finally, the importance of each feature is calculated based on interpretable machine learning. The proposed method is tested on two real-world datasets – smartphone and laptop reviews. The result reveals the changes in customer sentiments and preferences for product features, thus helping companies quickly establish strategies in rapidly changing market environments.

ANALYSIS OF CUSTOMER SENTIMENT ON PRODUCT FEATURES AFTER THE OUTBREAK OF CORONAVIRUS DISEASE (COVID-19) BASED ON ONLINE REVIEWS

AbstractThe outbreak of the coronavirus disease not only caused many deaths worldwide but also severely affected the development of the global economy, such as supply chain disruptions, plummeted demand, unemployment, etc. These social changes have led to changes in customers' purchasing patterns. Therefore, it is more important than ever for manufacturers to quickly identify and respond to changing customer purchasing patterns and requirements. However, few studies have been done on dynamic changes in customer preferences for product features following COVID-19 spread. This study aims to investigate the dynamic change of customer sentiment on product features following COVID-19 through sentiment analysis based on online reviews. The proposed methodology consists of two main processes: feature extraction and sentiment analysis. After finding a specific feature of the product through feature extraction, the words used to mention the feature in the review were analyzed for sentiment analysis of customers. To demonstrate the methodology, a case study is conducted using new and refurbished smartphone reviews to investigate the dynamic changes in customer sentiment during COVID-19.

Stakeholder Feedback of Electronic Medication Adherence Products: Qualitative Analysis.

BackgroundMedication management among older adults continues to be a challenge, and innovative electronic medication adherence products have been developed to address this need.ObjectiveThe aim of this study is to examine user experience with electronic medication adherence products, with particular emphasis on features, usefulness, and preferences.MethodsOlder adults, caregivers, and health care providers tested the usability of 22 electronic medication adherence products. After testing 5 products, participants were invited to participate in a one-on-one interview to investigate their perceptions and experiences with the features, usefulness, and preference for electronic medication adherence products tested. The interviews were audio recorded, transcribed, and analyzed using exploratory inductive coding to generate themes. The first 13 interviews were independently coded by 2 researchers. The percentage agreement and Cohen kappa after analyzing those interviews were 79% and 0.79, respectively. A single researcher analyzed the remaining interviews.ResultsOf the 37 participants, 21 (57%) were older adults, 5 (14%) were caregivers, and 11 (30%) were health care providers. The themes and subthemes generated from the qualitative analysis included product factors (subthemes: simplicity and product features, including availability and usability of alarms, portability, restricted access to medications, and storage capacity) and user factors (subthemes: sentiment, affordability, physical and cognitive capability, and technology literacy and learnability).ConclusionsElectronic medication adherence products have the potential to enable independent medication management in older adults. The choice of a particular product should be made after considering individual preferences for product features, affordability, and the sentiment of the users. Older adults, caregivers, and health care providers prefer electronic medication adherence products that are simple to set up and use, are portable, have easy-to-access medication compartments, are secure, and have adequate storage capacity.

Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social Media Networks

Lead users play a vital role in next generation product development, as they help designers discover relevant product feature preferences months or even years before they are desired by the general customer base. Existing design methodologies proposed to extract lead user preferences are typically constrained by temporal, geographic, size, and heterogeneity limitations. To mitigate these challenges, the authors of this work propose a set of mathematical models that mine social media networks for lead users and the product features that they express relating to specific products. The authors hypothesize that: (i) lead users are discoverable from large scale social media networks and (ii) product feature preferences, mined from lead user social media data, represent product features that do not currently exist in product offerings but will be desired in future product launches. An automated approach to lead user product feature identification is proposed to identify latent features (product features unknown to the public) from social media data. These latent features then serve as the key to discovering innovative users from the ever increasing pool of social media users. The authors collect 2.1 × 109 social media messages in the United States during a period of 31 months (from March 2011 to September 2013) in order to determine whether lead user preferences are discoverable and relevant to next generation cell phone designs.

Predicting customer purchase behavior in the e-commerce context

Predicting customer purchase behavior is an interesting and challenging task. In the e-commerce context, meeting this challenge requires confronting many problems not observed in the traditional business context. Recommender system technology has been widely adopted by e-commerce websites. However, a traditional recommendation algorithm cannot perform well the predictive task in this context. This study intends to build a predictive framework for customer purchase behavior in the e-commerce context. This framework, known as CustOmer purchase pREdiction modeL (COREL), may be understood as a two-stage process. First, associations among products are investigated and exploited to predicate customer's motivations, i.e., to build a candidate product collection. Next, customer preferences for product features are learned and subsequently used to identify the candidate products most likely to be purchased. This study investigates three categories of product features and develops methods to detect customer preferences for each of these three categories. When a product purchased by a particular consumer is submitted to COREL, the program can return the top n products most likely to be purchased by that customer in the future. Experiments conducted on a real dataset show that customer preference for particular product features plays a key role in decision-making and that COREL greatly outperforms the baseline methods.

A Hybrid Personalized Movie Recommender Based on Heterogeneous Preferences

Recommender systems — which filter information based on individual interests — represent one possible remedy for the problem of information overload. There are two major types of recommendation techniques — collaborative filtering and content-based filtering. While the content-based approach alleviates the “cold start” problem faced by collaborative filtering, its accuracy is generally lower. Therefore, a hybrid approach is commonly adopted. However, we find that the existing hybrid approaches are hampered by insufficient analysis of unstructured content features of recommended products and the neglects of heterogeneous individual preferences on product attributes. Therefore, we propose a new recommendation framework that utilizes latent semantic analysis to extract semantic features from unstructured text and employs multiple regression to identify unique product feature preferences for each user. By using a combined dataset from MovieLens and Microsoft Xbox, we develop a movie recommender as a proof of concept. The initial results represent a promising opportunity for combining behavioral studies and computer algorithms.

Understanding and modelling heterogeneity of human preferences for engineering design

In today's competitive market, it is essential for companies to provide products which not only achieve high performance, but also appeal to the tastes of consumers. Therefore, a key element of design is an understanding of consumer preferences for product features. In this work, the random-effects ordered logit model is proposed as the modelling framework to capture the impact of both product and human attributes on consumers’ ratings of qualitative system and sub-system attributes. To support the modelling, a series of methodologies are developed to both understand and model the influence of consumer heterogeneity upon product preferences. To illustrate the methodologies, a human appraisal experiment for understanding preferences for automobile occupant package design is analysed. An issue with analysing human appraisal experiments is that the effect of respondent heterogeneity must be understood to separate the influence of design factors from that of human factors. Hierarchical Bayes estimation and cluster analysis are used to gain an understanding of respondent rating styles, which are subsequently modelled explicitly in the ordered logit model. Smoothing spline regression is used to determine the functional form of the ordered logit model. The proposed ordered logit model is validated using a vehicle design case study.

Reality Check: Combining Choice Experiments with Market Data to Estimate the Importance of Product Attributes

Discrete choice models estimated using hypothetical choices made in a survey setting (i.e., choice experiments) are widely used to estimate the importance of product attributes in order to make product design and marketing mix decisions. Choice experiments allow the researcher to estimate preferences for product features that do not yet exist in the market. However, parameters estimated from experimental data often show marked inconsistencies with those inferred from the market, reducing their usefulness in forecasting and decision making. We propose an approach for combining choice-based conjoint data with individual-level purchase data to produce estimates that are more consistent with the market. Unlike prior approaches for calibrating conjoint models so that they correctly predict aggregate market shares for a “baseline” market, the proposed approach is designed to produce parameters that are more consistent with those that can be inferred from individual-level market data. The proposed method relies on a new general framework for combining two or more sources of individual-level choice data to estimate a hierarchical discrete choice model. Past approaches to combining choice data assume that the population mean for the parameters is the same across both data sets and require that data sets are sampled from the same population. In contrast, we incorporate in the model individual characteristic variables, and assert only that the mapping between individuals' characteristics and their preferences is the same across the data sets. This allows the model to be applied even if the sample of individuals observed in each data set is not representative of the population as a whole, so long as appropriate product-use variables are collected that can explain the systematic deviations between them. The framework also explicitly incorporates a model for the individual characteristics, which allows us to use Bayesian missing-data techniques to handle the situation where each data set contains different demographic variables. This makes the method useful in practice for a wide range of existing market and conjoint data sets. We apply the method to a set of conjoint and market data for minivan choice and find that the proposed method predicts holdout market choices better than a model estimated from conjoint data alone or a model that does not include demographic variables.

Reality Check: Combining Survey and Market Data to Estimate Choice Models

Discrete choice models estimated using hypothetical choices made in a survey setting (e.g., choice-based conjoint) are widely used to forecast the effects of product design and marketing mix decisions. Survey methods allow the researcher to estimate preferences for product features that do not yet exist in the market. However, parameters estimated from survey data often show marked inconsistencies with marginal effects inferred from the market, reducing their usefulness in forecasting and decision making. Several methods for adjusting survey-based choice models so that they more accurately predict market share have been suggested, but existing calibration methods are ad hoc and may change parameter values in ways that render them less consistent with other key empirical features of the data. We propose a new approach that produces more market-consistent parameter estimates by combining individual-level purchase data from the market with survey choice data in the formal estimation process.The proposed method relies on a new general framework for combining two or more sources of choice data to estimate a hierarchical discrete choice model. Past approaches to combining choice data assume that the population mean for the parameters is the same across both data sets and require that data sets are sampled from the same population. In contrast, we incorporate individual demographic and product-use variables into the model and assert only that the mapping between individuals' demographics and their preferences is the same across the data sets. This allows the model to accommodate differences in choice behavior across the data sets driven by differences in observed demographics. The framework also explicitly incorporates a model for the individual demographics which allows us to use Bayesian missing data techniques to handle the situation where each data set contains different demographic variables. This makes the method useful in practice for a wide range of existing market and survey data sets. We apply the method to a set of conjoint and market data for minivan choice and find that the proposed method predicts holdout market choices better than a model estimated from conjoint data alone or a model that does not include demographic variables.

Credit Card Disclosures, Solicitations, and Privacy Notices: Survey Results of Consumer Knowledge and Behavior

The mandatory dissemination of certain information by financial institutions is a key aspect of consumer protection law. It offers two significant advantages for consumer protection in the financial area over the alternative of direct government intervention into product pricing and content. First, information disclosure is compatible with competition, a significant market force already at work to protect consumers by keeping price rises in check. Because of competition, institutions already have incentives to make their products known, to reveal favorable pricing and product features, and to treat consumers fairly by keeping them generally informed about what they want and need to know. When a financial institution employs these strategies, it generates a good business reputation that will produce referrals and repeat customers. Actions that firms use to accomplish these goals include advertising their prices and supplying clients and potential customers with useful information about product prices and features. The requirements for disclosures assist in the dissemination of financial information by standardizing concepts and terminology, such as the finance charge and annual percentage rate under the Truth in Lending Act and the annual percentage yield under the Truth in Savings Act. Such standardization advances consumers’ knowledge about pricing and features of the financial products and institutions and lowers consumers’ transactions costs by making shopping easier. The standard format of required disclosures helps highlight the performance of the best institutions and exposes the inadequacies of the poorer ones. Well-informed shoppers help keep markets competitive, which benefits buyers of products and services by minimizing the spread between producers’ production costs and market price.1 The second advantage of information disclosure over direct intervention through mandating specific product pricing or features is that the government need not know, or presume to know, the product feature preferences of all consumers. With effective disclosures, consumers can decide what their preferences are in the tradeoff between price and product features; the success of the disclosure approach to consumer protection does not depend on consumers’ preferences being the same. Disclosure requirements may also be less costly for financial institutions to implement and for the government to enforce than consumer protection approaches that limit product features.

A comparison of quality function deployment and conjoint analysis in new product design

In this work, we compare two product design approaches, quality function deployment (QFD) and conjoint analysis, by applying each to the design of a new all-purpose climbing harness for the beginning/intermediate ability climber that would complement a leading manufacturer’s existing product line. While many of the optimal design features were the same under both approaches, the differences allow us to highlight the strengths of each approach. With conjoint analysis, it was easier to compare the most preferred features (i.e., ones that maximized sales) to profit maximizing features and also to develop designs that optimize product line sales or profits. On the other hand, QFD was able to highlight the fact that certain engineering characteristics or design features had both positive and negative aspects. This tradeoff could point the way to “out of the box” solutions. QFD also highlighted the importance of starting explicitly with customer needs, regardless of which method is used. Rather than competing, we view them as complementary approaches that should be conducted simultaneously; each providing feedback to the other. When the two approaches differed on the optimal level or importance of a feature, it appeared that conjoint analysis better captured customers’ current preferences for product features while QFD captured what product developers thought would best satisfy customer needs. Looking at the problem through these different lenses provides a useful dialogue that should not be missed. QFD’s ability to generate creative or novel solutions should be combined with conjoint analysis’ ability to forecast market reaction to design changes.

A comparison of quality function deployment and conjoint analysis in new product design

In this work, we compare two product design approaches, quality function deployment (QFD) and conjoint analysis, by applying each to the design of a new all‐purpose climbing harness for the beginning/intermediate ability climber that would complement a leading manufacturer's existing product line. While many of the optimal design features were the same under both approaches, the differences allow us to highlight the strengths of each approach. With conjoint analysis, it was easier to compare the most preferred features (i.e., ones that maximized sales) to profit maximizing features and also to develop designs that optimize product line sales or profits. On the other hand, QFD was able to highlight the fact that certain engineering characteristics or design features had both positive and negative aspects. This tradeoff could point the way to “out of the box” solutions. QFD also highlighted the importance of starting explicitly with customer needs, regardless of which method is used.Rather than competing, we view them as complementary approaches that should be conducted simultaneously; each providing feedback to the other. When the two approaches differed on the optimal level or importance of a feature, it appeared that conjoint analysis better captured customers' current preferences for product features while QFD captured what product developers thought would best satisfy customer needs. Looking at the problem through these different lenses provides a useful dialogue that should not be missed. QFD's ability to generate creative or novel solutions should be combined with conjoint analysis' ability to forecast market reaction to design changes. © 2002 Elsevier Science Inc. All rights reserved.

A Nonparametric Approach to Identifying Latent Relationships in Hierarchical Models

This paper provides a method for nonparametrically modeling the relationship between consumer preference for product features, such as reliability or durability, and covariates that describe consumers and how they use the product. This relationship is of interest to firms designing and delivering products to a market because the extent to which consumers are sensitive to particular features determines the potential profitability of product offerings, and affects decisions relating to appropriate distribution outlets and advertising strategies. The successful identification of these relationships also aids in efficiently targeting marketing activities to specific segments of the consumer population. The relationship between consumer preference for product features and observable covariates is important but is typically unknown. In addition, these relationships are often deeply embedded in a model hierarchy and are not observed directly. For example, in models of household choice, the observed outcomes are multinomial with probabilities driven by latent utilities or values that consumers place on the choice alternatives. These utilities are in turn a function of characteristics, such as price and product features, which are differentially valued. Of primary interest is the relationship between consumer sensitivity to product characteristics and readily observed covariates such as household demographics or aspects of product usage. Because the relationships of interest are not directly observed, it is difficult to draw inferences about them without formal statistical models. This paper presents a three-level hierarchical Bayes model for modeling binary consumer preferences as a function of observable covariates. The hierarchical model nonparametrically estimates the relationships between consumer preferences for product features and the covariates without assuming a specific functional form. A nonparametric model is particularly useful in the exploratory analysis of consumer data in which the primary purpose of the analysis is to generate further questions rather than provide specific answers to well-posed questions. This type of analysis is frequently encountered in marketing where a series of studies are commissioned to better understand the nature of demand. The first level of the hierarchy in the Bayesian model relates the binary consumer choice to the sensitivities of the consumer to product attributes such as brand name, price, reliability, and durability. The second level of the hierarchy models the heterogeneity across consumers using functions that relate attribute sensitivities to observable covariates. This level of the hierarchy also allows each respondent to have unique demand coefficients by introducing random effect components. The third level of the hierarchy specifies a smoothness prior for each of the unknown functions used in the second level. The approach is flexible and works well both when the unknown function can be closely approximated by a linear function and when it cannot be. A Bayesian model selection technique is used to determine which functions can be modeled using a linear function and which ones should be modeled nonparametrically to provide the necessary flexibility to estimate the function accurately. The proposed methodology is illustrated using data from a survey of consumer preferences for features of marine outboard engines that was collected as part of a consulting project. Our analysis focuses on measuring consumer preferences for engine features and their relationships to two variables related to boat length and engine size. Consumer preferences for engine features were obtained through a national survey conducted over the telephone. Preferences were elicited by means of a pairwise evaluation in which respondents chose between two engines that were identical in every respect except for two engine features. The methodology can be modified to allow for more complex comparisons such as conjoint data collected in full profiles. The application of a Bayesian model selection procedure indicates that 4 of the 28 covariate relationships in the model are nonlinear, while the other 24 are linear. The preferences associated with these four functions are involved in 56% of the pairwise comparisons in the study. Therefore, in practice, if the nonlinear functions are not properly estimated there is the potential to draw misleading inferences regarding 56% of the pairwise choices. Firms can use the estimates of the functions relating preferences to covariates in a number of ways. First, they can use the covariates to determine the total number of consumers who have high demand for a particular product feature, and then they can target communication efforts to those individuals. Alternatively, the empirical results can be used as a basis of subsequent analysis to obtain a more complete characterization of a market segment.

Using Conjoint Analysis to Help Design Product Platforms

This article illustrates how one can combine different conjoint analysis studies, each containing a core of common attributes, to help design product platforms that serve as the foundation for multiple derivative products. The illustration is based on actual, but disguised, data from a small company that makes electronic test equipment.This article demonstrates that decisions that consider products individually are likely to be suboptimal and can be significantly different than those based on product platforms. Suboptimality can occur either when preferences for product features differ across markets or when a technology is more important to the overall company than it is to an individual product. Additionally, we show the importance of considering both fixed and variable costs when performing this type of analysis as sales, contribution, and profit‐maximizing products are quite different. Finally, sensitivity analyses show that these results are robust with respect to assumptions about price sensitivity, fixed costs, and timing of entry. © 1999 Elsevier Science Inc.